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Christopher Michael Bishop was born on 7 April 1959 in Norwich, England, to Leonard and Joyce Bishop. [7] He was educated at Earlham School in Norwich, and obtained a Bachelor of Arts degree in physics from St Catherine's College, Oxford, and later a PhD in theoretical physics from the University of Edinburgh, [7] with a thesis on quantum field theory supervised by David Wallace and Peter Higgs.
Christopher Bishop is an American mathematician on the faculty at Stony Brook University.He received his bachelor's in mathematics from Michigan State University in 1982, going on from there to spend a year at Cambridge University, receiving at Cambridge a Certificate of Advanced Study in mathematics, before entering the University of Chicago in 1983 for his doctoral studies in mathematics.
2020 – Christopher Bishop; 2021 – Peter Bartlett; In 2015, the NeurIPS Board introduced the Breiman Lectureship to highlight work in statistics relevant to conference topics. The lectureship was named for statistician Leo Breiman, who served on the NeurIPS Board from 1994 to 2005. [10] Past lecturers have included: 2015 – Robert Tibshirani
The library is designed to reduce computing power and memory use and to train large distributed models with better parallelism on existing computer hardware. [2] [3] DeepSpeed is optimized for low latency, high throughput training.
A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.
A deep CNN of (Dan Cireșan et al., 2011) at IDSIA was 60 times faster than an equivalent CPU implementation. [12] Between May 15, 2011, and September 10, 2012, their CNN won four image competitions and achieved SOTA for multiple image databases. [13] [14] [15] According to the AlexNet paper, [1] Cireșan's earlier net is "somewhat similar."
The information bottleneck method is a technique in information theory introduced by Naftali Tishby, Fernando C. Pereira, and William Bialek. [1] It is designed for finding the best tradeoff between accuracy and complexity (compression) when summarizing (e.g. clustering) a random variable X, given a joint probability distribution p(X,Y) between X and an observed relevant variable Y - and self ...
In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis. When representing images, the feature values ...